Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.

Journal: PloS one
PMID:

Abstract

PURPOSE: This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.

Authors

  • Haoran Zhang
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Jinlong Liu
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Danyang Su
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Zhen Bai
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
  • Yan Wu
    Beijing Hui-Long-Guan Hospital, Peking University, Beijing, 100096, China.
  • Yuanbo Ma
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
  • Qiuju Miao
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China.
  • Mingyue Wang
  • Xiaopeng Yang
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.